Depth-Aware Implicit Neural Representation Priors for 3D Gravity Inversion

• URL: http://arxiv.org/abs/2608.08959v1
• Date: Sun, 09 Aug 2026 23:34:32 GMT
• Title: Depth-Aware Implicit Neural Representation Priors for 3D Gravity Inversion
• Abstract summary: This paper proposes an unsupervised depth-aware implicit neural representation for 3D gravity inversion.<n> Experiments show that the proposed method provides better overall performance in terms of RMSE, PSNR, and SSIM.<n>In the field experiment, where no ground-truth density model was available, the method produced compact, separated, and vertically coherent anomalies.
• Score: 19.44527990597079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gravimetry images subsurface density contrasts associated with geological structures, geothermal systems, and intrusive bodies. Recovering a three-dimensional density model from gravity observations is highly ill-posed because of its non-uniqueness, limited data coverage, and the attenuation of the gravity field with depth. Classical inversion methods rely on explicit regularization and parameter tuning, whereas supervised deep-learning approaches require representative gravity--density pairs that are rarely available. This paper proposes an unsupervised depth-aware implicit neural representation for 3D gravity inversion. The density volume is represented by multiple coordinate-based neural networks assigned to overlapping depth slabs and optimized directly from the observed gravity measurements through the sensitivity matrix. Slab-specific Fourier features, physics-based depth gains, and scheduled regularization provide structural priors without requiring labeled density models. Experiments on four synthetic scenarios show that the proposed method provides better overall performance in terms of RMSE, PSNR, and SSIM than the evaluated conventional and neural baselines. It also recovers more compact and spatially coherent density bodies, improves the separation of nearby anomalies, preserves internal structures, and reconstructs their vertical extent better. These results indicate that the proposed depth-aware formulation helps to mitigate the depth ambiguity inherent in gravity inversion. In the field experiment, where no ground-truth density model was available, the method produced compact, separated, and vertically coherent anomalies consistent with the observed gravity pattern.

Related papers

• Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine [60.669081685261965]
  Diffusion models generate high-dimensional data with remarkable quality.<n>How their training efficiently learns the score function remains theoretically unexplained.<n>We instantiate this principle as Score-induced Latent Diffusion (SiLD)
  arXiv  Detail & Related papers  (2026-05-16T16:51:10Z)
• Topo-GS: Continuous Volumetric Embedding of High-Dimensional Data via Topological Gaussian Splatting [4.621191941382847]
  Topo-GS is a framework that repurposes 3D Gaussian Splatting (3DGS) to cast multidimensional projection as a meshless volumetric reconstruction process.<n>We show that Topo-GS successfully transforms discrete scatter plots into continuous volumetric representations.
  arXiv  Detail & Related papers  (2026-05-16T14:21:08Z)
• DOC-GS: Dual-Domain Observation and Calibration for Reliable Sparse-View Gaussian Splatting [80.43237927269575]
  In this paper, we revisit sparse-view 3DGS reconstruction from a new perspective.<n>We identify the core challenge as the unobservability of Gaussian primitive reliability.<n>Motivated by this observation, we propose a unified Dual-domain Observation and geometrics in rendered images framework.
  arXiv  Detail & Related papers  (2026-04-08T07:01:24Z)
• In Depth We Trust: Reliable Monocular Depth Supervision for Gaussian Splatting [22.674046124865196]
  We introduce a training framework integrating scale-ambiguous and noisy depth priors into geometric supervision.<n>Experiments across diverse datasets show consistent improvements in geometric accuracy.
  arXiv  Detail & Related papers  (2026-04-07T11:15:15Z)
• MDE-VIO: Enhancing Visual-Inertial Odometry Using Learned Depth Priors [8.2208199207543]
  We propose a novel framework that enforces affine-invariant depth consistency and pairwise ordinal constraints.<n>This approach strictly adheres to the computational limits of edge devices while robustly recovering metric scale.
  arXiv  Detail & Related papers  (2026-02-11T19:53:06Z)
• ERGO: Excess-Risk-Guided Optimization for High-Fidelity Monocular 3D Gaussian Splatting [63.138778159026934]
  We propose an adaptive optimization framework guided by excess risk decomposition, termed ERGO.<n> ERGO dynamically estimates the view-specific excess risk and adaptively adjust loss weights during optimization.<n>Experiments on the Google Scanned Objects dataset and the OmniObject3D dataset demonstrate the superiority of ERGO over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-02-10T20:44:43Z)
• Three-dimensional inversion of gravity data using implicit neural representations [2.7372527234669115]
  Inversion of gravity data is an important method for investigating subsurface density variations relevant to diverse applications.<n>Here we present a scientific machine-learning approach for three-dimensional gravity inversion that represents subsurface density as a continuous field.
  arXiv  Detail & Related papers  (2025-10-17T03:55:08Z)
• Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian [49.21866794516328]
  3D Gaussian splatting has demonstrated impressive performance in real-time novel view synthesis. Previous approaches have incorporated depth supervision into the training of 3D Gaussians to mitigate overfitting. We introduce a novel method to supervise the depth distribution of 3D Gaussians, utilizing depth priors with integrated uncertainty estimates.
  arXiv  Detail & Related papers  (2024-05-30T03:18:30Z)
• 3D Density-Gradient based Edge Detection on Neural Radiance Fields (NeRFs) for Geometric Reconstruction [0.0]
  We show how to generate geometric 3D reconstructions from Neural Radiance Fields (NeRFs) using density gradients and edge detection filters. Our approach demonstrates the capability to achieve geometric 3D reconstructions with high geometric accuracy on object surfaces and remarkable object completeness.
  arXiv  Detail & Related papers  (2023-09-26T09:56:27Z)
• Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines [116.33694183176617]
  We use a neural network based on the Recurrent Inference Machine to reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions.
  arXiv  Detail & Related papers  (2023-01-10T19:00:12Z)
• Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography [58.60249163402822]
  Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations. The proposed OMR is more robust and performs significantly better than the previous state-of-the-art OMR approach.
  arXiv  Detail & Related papers  (2022-07-06T21:40:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.